Improved Biomass Calibration and Validation With Terrestrial LiDAR: Implications for Future LiDAR and SAR Missions

Future NASA and ESA satellite missions plan to better quantify global carbon stocks through detailed observations of forest structure, but ultimately rely on uncertain ground measurement approaches for calibration and validation. A substantial amount of uncertainty in estimating plot-level biomass can be attributed to inadequate and unrepresentative allometric relationships used to convert plot-level tree measurements to estimates of aboveground biomass. These allometric equations are known to have high errors and biases, particularly in carbon-rich forests, because they were calibrated with small and often biased samples of destructively harvested trees. To overcome this issue, we present and test a framework for nondestructively estimating tree and plot-level biomass with terrestrial laser scanning (TLS). We modeled 243 trees from 12 species with TLS and created ten low-RMSE allometric equations. The full 3-D reconstructions, TLS allometry, and Jenkins et al. (2003) allometry were used to calibrate SAR- and LiDAR-based empirical biomass models to investigate the potential for improved accuracy and reduced uncertainty. TLS reduced plot-level RMSE from 18.5% to 9.8% and revealed a systematic negative bias in the national equations. At the calibration stage, allometric uncertainty accounted for 2.8-28.4% of the total RMSE, increasing in relative contribution as calibration improved with sensor fusion. Our findings suggest that TLS plot acquisitions and nondestructive allometry can play a vital role for reducing uncertainty in calibration and validation data for biomass mapping in the upcoming NASA and ESA missions.